1.2.1 A General Group Intelligence Factor

Historically, the invention of the intelligence test establishes the intelligence concept. In 1905, Alfred Binet designed the first version of this test. It identified French schoolchildren with learning disabilities who needed more support than other children (Binet, Simon, & Kite, Reference Binet, Simon and Kite1916). At the same time, Charles Spearman (Reference Spearman1904) developed the theory of general intelligence (or “g”) that proposed that a large part of a person’s intelligence was built on a general problem-solving ability. It would persist for many years before more complex definitions of intelligence were accepted (Piaget, Reference Piaget1952). In recent time, there have also been attempts to extend the notion of intelligence beyond its focus on human cognition. For example, Howard Gardner (Reference Gardner1983) described the existence of seven different types of intelligence in his book Frames of Mind: The Theory of Multiple Intelligences. Three types covered cognitive abilities (linguistic intelligence, logical-mathematical intelligence, and spatial intelligence), and the four others, musical intelligence, bodily-kinesthetic intelligence, interpersonal intelligence, and intrapersonal intelligence, were new types of intelligence. Intrapersonal intelligence focuses on the capacity to have knowledge about oneself and control personal emotions, while socially orientated interpersonal intelligence describes the ability to understand and collaborate with other people. Still, human intelligence today is primarily connected to cognitive abilities and skills.

In contrast, CI research by Woolley et al. (2010) have found evidence of a general group intelligence factor, labeled the “c factor,” in different types of group work. This has even led to the development of a group intelligence test, which is different from the cognitive tasks that are typical in standardized individual intelligence tests. The test tasks cover four different dimensions in authentic settings. The first task is about generating something new, like brainstorming various uses for a brick. The second category involves the selection of a pre-specified alternative, making groups solve visual puzzles from a standardized test called Raven’s Matrices. The third dimension includes negotiating tasks, challenging the group to pretend they live together and have to plan a shopping trip. The fourth dimension is about executing tasks, and letting the group type a long text passage through synchronous online writing. In addition, other tasks involve word-completion problems, spatial puzzles, and estimation problems (Malone, Reference Malone2018: 31).

In the original study, 152 groups of two to five members were set to solve a wide range of different tasks. Factor analysis of team scores identified one factor accounting for 44 percent of the variance, while the second factor only explained 20 percent of the scores. Here, collective intelligence is the inference one draws when the ability of a group to perform one task is correlated with that group’s ability to perform a wide range of other tasks. The first factor with significant explanatory power is interpreted as a property of the group itself, not just the individuals in it. Nor was this factor correlated with the average or maximum individual intelligence of group members (Woolley et al., Reference Woolley, Chabris, Pentland, Hashmi and Malone2010). Other follow-up studies have shown similar results in other settings across different languages, cultures, and activities (Malone, Reference Malone2018: 32–42; Woolley et al., Reference Woolley, Aggarwal and Malone2015). For example, in high-performing teams playing online video games, collective intelligence scores were significant predictors of their performance in the game (Kim et al., Reference Kim, Engel, Woolley, Lin, McArthur and Malone2017). The “c factor” has also predicted performance for other more complex tasks such as playing checkers against a computer or solving architectural design problems. In addition, the highly collectively intelligent teams exhibited steady improvement in performance across the series of tests, indicating that these groups also learn faster (Malone, Reference Malone2018: 32–42; Woolley et al., Reference Woolley, Aggarwal and Malone2015).

According to Malone (Reference Malone2018: 41), the combination of all these studies indicate that human groups have a kind of collective intelligence that is directly analogous to what is measured by individual intelligence tests. He highlights the distinction between (1) specialized intelligence and (2) general intelligence in individual intelligence tests (Malone, Reference Malone2018: 24). First, specialized intelligence refers to the ability to achieve specific goals effectively in a given environment. The equivalent of this type at a group level will then be “group effectiveness.” However, intelligence tests have been designed to predict your general intelligence or your ability to do a wide range of other tasks beyond those in the test. People who have much of this general intelligence are better at adapting to new environments and learn more quickly. Likewise, general collective intelligence refers to the group’s ability to adapt to new environments and perform well on a wide range of different group tasks (Malone, Reference Malone2018: 24–25, 41).

Although some researchers claim there is insufficient support for the existence of a collective intelligence construct (e.g. Bates & Gupta, Reference Bates and Gupta2017; Credé & Howardson, Reference Credé and Howardson2017; Woolley, Kim, & Malone, Reference Woolley, Kim and Malone2018), there is increased interest in the more general problem-solving abilities in groups in both offline and online settings. However, we still know little about which group processes or group qualities influence the “c factor.” There are affiliated concepts such as group cognition and group mind. Within sociology, both Durkheim’s concept of collective consciousness and Gabriel Tarde’s notion of group mind move beyond the individual self in their examination of societal beliefs in larger groups. In psychology, new theories of learning also highlight the qualities of group discourse and joint meaning making to a greater degree (Sawyer, 2006; Stahl, Reference Stahl2006). Knowledge does not reside inside the heads of individuals, but in the practice itself (Flick, 1998; Gergen, 1985). Likewise, this book analyzes CI as a group phenomenon.

1.2.2 Self-Organization

Another strand of CI research examines different types of self-organization. The first type of self-organization is at a macro level, describing the Internet as a self-organizing super-intelligence that unites all human intelligence into a worldwide network of information and communication. For example, Heylighen (Reference Hayek2011) uses the metaphor of a global brain to describe the Internet as an intelligent, organism-like system, a brain of brains. CI emerges from the collective interactions between humans and machine in a global online communication network. This global brain is immensely complex and self-organizing without any centralized control, and emerges as an adaptive complex system. In an interview (Lollini et al., 2019), Levy claims this type of self-organization can best be described as stigmergic communication. Throughout our human history, improvements in CI has followed from inventions that augmented the power of human language. The invention of writing created a new collective memory that was further developed with the invention of the printing press. Moreover, the invention of the Internet completely removes the constraints of physical space and memory when knowledge becomes accessible from anywhere in the world. This is not only communication from many to many, but also a new way of connecting knowledge when it is stored in an online setting. The stigmergic element refers to the intermediary of a common shared environment that everyone uses. Almost the entirety of humanity can add knowledge to this shared memory, which anyone can access. In addition, every new trace of action on the Internet will continuously change the relationship between the stored digitized data. In this sense, everybody contributes to the transformation of the common memory at the same time. Although CI is facing huge challenges today, Levy proposes that the way forward is to design practices that can promote reflective communication between people in the online setting (Lollini et al., Reference Lollini, Farley and Levy2019).

The second type of self-organization describes the emergence of global online communities. One example is Wikipedia, which has more than six million articles in the English version alone (Rijshouwer, Reference Rijshouwer2019). Another example is the development of open source software where many individuals contribute at different points of time (Raymond, Reference Raymond1999). Mulgan (Reference Mueller2018: 76) also describes how stigmergy is important in self-organizing systems like Wikipedia, or among open source software development programmers who pass around tasks in the form of challenges until they find a volunteer. Human stigmergic problem solving is an important part of the analysis in this book (see Chapter 6).

Third, self-organization can build on market mechanisms, like the “invisible hand” that self-regulates the market economy by letting everyone pursue their own interests (Hayek, 2013). Widely dispersed markets use price signals efficiently to coordinate large-scale activities. Markets can adjust prices with little horizontal communication between the participants, but they are limited to the binary decision of whether or not to buy something (Mulgan, Reference Mueller2018: 111, 115). In CI research, this type of self-organization has been examined in studies of prediction markets (Buckley & O’Brien, 2017; Malone, Reference Malone2018) which is also a topic addressed in this book (see Section 6.3).

A fourth type of self-organization studies swarm problem solving in animals. Peters and Heraud (Reference Peters and Heraud2015) claim biological studies of “swarm intelligence” is one of six major areas within CI. It refers to the collective behavior of social insects and flocking behavior (Mulgan, Reference Mueller2018: 232). For example, Sumpter (Reference Sumpter2010) claims human collective behavior can be explained through self-organization and different behavioral algorithms. These principles, such as positive feedback, response thresholds, and independent decision-making, are also present in different animal groups and can inform our understanding of human societies. However, Willcox, Rosenberg, and Domnauer (2020), claim there is no good theory that explains how human swarms operate. Few studies examine large-scale human collective work in the offline setting. This area of investigation is labeled as human swarm problem solving in this book (see Chapter 4).

1.2.3 The Role of Diversity

In general, CI expects that new technologies will make groups better at solving problems than ever before (Malone, Reference Malone2018). The predominant strategy is to scale up the size of the group and hope this can create more diversity benefits. A prominent example is the book The Wisdom of Crowds by Surowiecki (Reference Surowiecki2005), which describes four qualities that make a crowd intelligent. First, the group should be diverse, so different individuals can supplement each other with different pieces of information. Second, the group needs to be decentralized, without anyone directing the answers from the center. Third, individual opinions need to be aggregated into a collective opinion. Aggregation typically depends on numerical contributions and statistical methods. Fourth, the individuals in the crowd should act independently of each other.

In a historical perspective, it was the British scientist Francis Galton who in 1906 first described the “Wisdom of the Crowd”-effect from a scientific perspective. He visited West of England Fat Stock and Poultry Exhibition, which had organized a contest where anyone could guess the weight of an ox. Eight hundred individuals made guesses, but none had the right answer. Galton decided to average all the guesses, and surprisingly discovered that the estimate of 1,198 pounds was only 0.8 percent different from the correct answer of 1,207 pounds (Galton, Reference Galton1907). It was the first scientific paper suggesting that a large group could be much more accurate than individuals.

Today, the diversity prediction theorem, developed by Hong and Page (Reference Hong and Page2004) represents perhaps the most important theoretical explanation of CI with its emphasis on diversity. The mathematical theorem explains the relationship between collective accuracy and the diversity of predictions based on expected errors. The theorem can be written as the following mathematical proof:

The Crowd’s square Error = Mean square error of individuals – Predictive Diversity (Hong & Page, Reference Hong and Page2004).

The theorem states that the error of a crowd equals the average squared error minus the predictive diversity. First, the mean square error is the average of the individual squared errors. It includes the errors each individual has made as a distance from the correct or true value (Page, 2014).

Second, the prediction diversity equals the average squared distance from the individual predictions to the average prediction. From a statistical perspective, this is the same as the variance or how widely spread the predictions are, but Page prefers to use diversity as a term to underline the importance of variations in the predictions. This is the crowd diversity dimension (Page, 2014).

Third, the crowd error is the squared error of the collective prediction. It equals the average squared error (crowd’s prediction) minus the predictive diversity. The crowd square error must always be smaller or equal to the mean square error. Consequently, the prediction of a crowd must always be better than or equal to the average prediction of its members. Much better prediction requires a larger degree of diversity, while a crowd that is only as good as its average member will have no predictive diversity. The main point is that when the diversity in a group is large, the crowd error will be small. Therefore, wise crowds will rely on not only individual accuracy (mean individual error) but also crowd diversity (Page, 2014: 1332).

Consider the following example. Two persons predict the annual snowfall in their hometown. Both deviate from the correct answer by 4 inches, making the squared error equal to 16. If both guess 4 inches too many or too few, there is no predictive diversity. The crowd error will then be 16, equal to the mean square error (Theorem score: 16 = 16 – 0). However, if one person predicts 4 inches too many and the other person 4 inches too few, the crowd will provide the correct answer because the diversity of predictions cancel each other out (Theorem score: 0 = 16 – 16) (Page, 2014).

In essence, this theorem points to cognitive or informational diversity being at the core of CI, and this book will further explore this topic in the discussion of different types of collective problem solving.

1.5.1 Step 1: Review Current CI Practices

In the first phase of this research process, I undertook an extensive search of CI literature to obtain an overview of the most common practices and characterizations of the phenomenon. This phase dates back to the reading of a report in 2011 (Malone et al., Reference Malone, Laubacher and Dellarocas2009). Over the following years, several new scholars and stakeholders introduced new models covering a range of fields and sectors; however, these models typically concern a relatively small part of the multidisciplinary field. Chapters 1–3 comprise a summary of these readings on CI. They constitute the main review of CI literature, dividing the field into crowdsourcing and open online knowledge sharing as two major areas. Within these areas, a few important CI practices have been selected to provide a more detailed account. This approach coincides with Mulgan (Reference Mueller2018: 236), who suggests that an emerging discipline should be descriptive and analytic, observing collective intelligence “in the wild” in finding the most successful CI practices.

Chapter 2 describes crowdsourcing, a process whereby problems are sent outside an organization to a large group of people – a crowd – who can help provide solutions (Surowiecki, Reference Surowiecki2005). Online citizen science and online innovation contests are of particular interest because of their societal value. Within innovation, the two selected examples are from IdeaConnection and Climate CoLab, two innovation intermediaries who host different types of online innovation contests. One of these contests, the IdeaRally, represents an interesting new crowdsourcing method that allows hundreds of experts to participate in a one-week long intensive idea building process. In online citizen science, Zooniverse (e.g. Galaxy Zoo) and Foldit are selected as two prominent, but contrasting examples. The online protein folding game Foldit stands out as a particularly successful project that show what amateur gamers can achieve. The game design combines human visual skills with computer power in solving protein-structure prediction problems by constructing three-dimensional structures. Most successful solutions are team performances or achievements made by the entire Foldit gaming community. All the examples in this chapter illustrate successful case stories, and the detailed analysis identifies basic problem-solving mechanisms in crowdsourcing.

Another important area in CI is open online knowledge sharing (see Chapter 3). Open sharing is becoming more important in all major sectors in society, including science, politics, education and innovation. This sharing includes both the domain of expert-produced scientific knowledge and massive amounts of citizen-produced practical knowledge. Because of lower publishing costs, Open Access has become the new dominant trend that makes research accessible to everyone. Increased production of open textbooks gives a more readable access to scientific knowledge and reaches a much wider audience. In addition, scientific knowledge construction processes are becoming transparent. This includes the establishment of many more open digital databases that allow anyone both to make their own contributions and get free access to all the data (e.g. citizen science project like eBird). There is also experimentation with making knowledge construction processes more open, both within scientific discourse (e.g. Polymath project) and the development of encyclopedic knowledge (e.g. Wikipedia). In addition, the recent decade has seen an enormous increase in amateur-produced practical knowledge, not only texts, but an abundance of images and videos. Enthusiasts share their skills and passions concerning any activity that might be of interest to other like-minded persons. It also includes the sharing of political opinions, for example with new digital technologies like argument mapping. Even some companies in the business sector have begun sharing more of its corporate knowledge.

These CI practices address aspects of what Peters and Heraud (Reference Peters and Heraud2015) label as social innovation, new social practices that aim to strengthen civil society by improving working conditions, education, community development or health. This approach assumes that complex social problems require the involvement of engaged citizens. Citizens are capable of both co-evaluating and co-creating public goods and services that can reform the public sector (Peters & Heraud, Reference Peters and Heraud2015).

My perusal of the literature suggests that both crowdsourcing and open online knowledge sharing are central to CI, areas often highlighted at conferences, in research papers or in books on the subject. However, this does not imply that the CI examples are mainstream today. One example is argument mapping, an interesting practice that is not widely used. Online innovation contests and online citizen science (Chapter 2) are new, but still relatively peripheral practices.

1.5.2 Step 2: Identifying CI as Three Types of Collective Problem Solving

Collective problem solving is the core term in this book about CI. The term covers a range of different practices across different group sizes and periods, while retaining a common emphasis on aspects of problem solving. The term differs slightly from what is typical among other CI researchers, who often underline communication, coordination or other system characteristics (e.g. Suran et al., Reference Suran, Pattanaik and Draheim2020). The main advantage with using problem solving as a term is that it intends to cover the “complete” intelligent process. My readings of biologically orientated CI research inspired me to distinguish between three types of collective problem solving: 1. collaborative, 2. human swarm and 3. human stigmergic. Research on both swarm behavior and stigmergy is relevant for a large range of collective practices today. Subsequently, I searched for additional research studies to enrich the descriptions of the particularities of these subtypes (Chapters 4, 6, and 8), even when these studies did not explicitly mention CI. The goal in the first part of the book is to give a detailed description of the basic mechanisms that characterize these three types of collective problem solving.

Chapter 4 discusses human swarm problem solving as a distinct subtype of CI with biological antecedents in nest siting among honeybees and flocking behavior. Building on recent biological research, this chapter discusses five mechanisms that are also relevant for human swarm problem solving. These mechanisms are decision threshold methods, averaging, large gatherings, heterogeneous social interaction and environmental sensing. Studies of collective animal behavior show that they often make decisions that build on statistical rules (e.g. averaging, threshold responses). Even when in a group, individuals will often seek and assess information independently of others with the intention of optimizing decisions through the “many wrongs principle” or the “many eyes principle.” Similarly, human “wisdom of the crowd” studies examine similar statistical rules and principles like the importance of making independent contributions. However, while early research on the wisdom of crowds addressed the importance of independent contributions, newer studies also examine the possible positive influence of dependent contributions. The increasing variety of crowdsourcing studies are in this chapter explained within the framework of different swarm mechanisms. In the summary, four basic characteristics of human swarm problem solving are highlighted: predefined problems, pre-specified problem-solving procedures, rapid time-limited problem solving, and individual learning.

Chapter 6 presents human stigmergic problem solving as a distinct “solution-centered” subtype of CI with biological antecedents in the trail-laying and nest-building of ants. Stigmergy describe how many individual agents are able to coordinate collective action only by leaving information in a shared environment. In this type of collective problem solving, a version of a solution will already exist, either partially or completely. The problem-solving process will, therefore, be a response that changes the existing version of a solution by rating it, in the case of, for example, an online video; re-estimating it through a prediction market; adapting it like an open textbook or completing it like a Wikipedia article. In human qualitative stigmergy, a preliminary part of a solution will be stored in the system or medium, and individuals will then respond to the unfinishedness of the solution in different ways. If many versions of a solutions already exist, human quantitative stigmergy can also be used to rate the most optimal solutions. In the online setting, solutions will be continuously compared with each other. These stored solutions can solve many different problems at various points of time.

Chapter 8 proposes collaborative problem solving as one of three distinct types of CI. Collaborative problem solving covers a wide range of disciplines and contexts, but this chapter primarily draws on studies that have explicitly used CI as a scholarly concept. The most important finding is the identification of a general group performance ability on a wide variety of tasks. This group performance is analyzed in relation to four dimensions that promote successful collaborative problem solving. First, “working well with others” is not only analyzed as an individual ability, but as a quality that emerges through the qualities of a symmetrical collaborative relationship. Second, “cognitive diversity” describes diverse repertoires in groups, also including multidisciplinary and multicultural diversity. Third, “equal participation” emphasizes that everyone in the group should be allowed to bring in their perspective, and group discussions need to be open-minded. Fourth, “joint coordination” is important in setting goals together, dividing tasks and choosing relevant problem-solving strategies. Solver experiences from online innovation teams exemplify how this type of CI can move forward in a highly relevant authentic online setting.

1.5.3 Step 3: A Historical Analysis of the Problem-Solving Types

The third step is a cultural-historical analysis of each of the three types of collective problem solving (Chapters 5, 7, and 9). It is assumed that all problem-solving types build on mechanisms that humans already use. Hence, both the historical and phylogenetical origins of these three types of collective problem solving and their inner contradictions are examined. In addition, this brief analysis explains how the different problem-solving types evolved in complexity until our present day. CI is analyzed as a practice that has evolved over time as humans have learned how to use increasingly advanced tools. Sources include various previous books and articles that describe the relevant historical practices.

Chapter 5 argues that the origins of human swarm problem solving can be traced back to group hunting, which required rapid problem solving during the hunt, but also planning activities. Collective actions build on synchronization in the sense that every contribution from individual hunters mattered. Another milestone was the emergence of premodern trade, which enabled human groups to utilize informational diversity from nonkin and even strangers. Knowledge was shared in new ways through large gatherings and trade networks. The third major achievement was the establishment of the first democracy in ancient Athens with institutions such as the Assembly of the People, the Council of 500 and the People’s Court. These institutions enabled a large number of individuals to engage in rapid problem solving in a formalized manner. Individuals from all over the Athenian territory met in the city to solve societal problems. These historical examples show that human swarm problem solving is also a story about our ability to solve problems in increasingly larger groups.

Chapter 7 trace the origin of human stigmergic problem solving back to the invention of writing. Knowledge could now be stored, reused and made accessible to others. A human collective memory was established which made it possible to develop more complex societies. However, it is the “copy-revolution” of the printing press that enables human stigmergy to be used at full scale throughout society. The reduced cost of making a book allowed for a much more flexible reuse and sharing of existing knowledge across wider geographical distances. All types of written knowledge could easily be copied and made accessible to many more readers. Human stigmergy evolved into new forms. Frozen stigmergic problem solving describes how existing solutions in book format were copied and reused at an unprecedented scale. The mass production of identical copies made it possible to spread the same message to everyone across large geographical areas. This led to a radical increase in available knowledge; people began to learn faster from each other because knowledge sharing was amplified. In addition, fluid stigmergic problem solving describes how knowledge products were not only copied, but they were improved through new book editions and translated, which further spurred collective knowledge advancement.

Chapter 9 argues that the origins of collaborative problem solving can be traced back to mutual collaboration, which built on the evolution of more advanced forms of gestural communication. Elaborative collaborative problem solving builds on this type of collaboration and requires proximate mutual interaction and sympathy between the collaborators. In contrast, rule-governed collaborative problem solving centers on an idea of fairness and requires that collaborators adhere to specific rules or norms in their collaboration. At least two collaborative cultures were key in the evolvement of this type of collaboration. First, stone tool learning required deliberate practice and the presence of a community of learners with norms. Explicit teaching and individual training built on purposeful activities that were considered valuable, a collaborative culture which over time made it possible to refine stone tools across generations. Second, it is likely that hunter-gatherer groups were important in the development of ideas on equal participation, building on reciprocity and norms that emphasized equal sharing of food. Calculated reciprocity represents a significant move away from the dominance of a few individuals in groups. Equal sharing of food required increased control of emotions and the establishment of norms that kept free riders out. A fair sharing of spoils also permitted role differentiation in groups because not everyone had to participate in the hunt in order to get food.

1.5.4 Step 4: Design of CI

The second part of the book examines in more detail how CI can be successful in the scientific and political domain. The analysis covers three interrelated dimensions: intelligent engagement (Chapter 10), intelligent contributions (Chapter 11), and intelligent evaluations (Chapter 12). On one hand, CI points to the emergence of Open Science, an umbrella term that describes a movement that aims to make scientific research accessible to everyone in society. This involves both citizen participation in scientific processes and the increased open sharing of scientific publications. On the other hand, CI orientates itself towards Open Democracy, which is a term used to describe how the Internet and digital technology can strengthen democratic participation and put ordinary citizens at the center of political systems in new ways.

Chapter 10 analyzes the relationship between citizen participation and citizen expertise, particularly in the political domain. New types of intelligent citizen engagement are emerging, such as mass deliberation, mass voting and social media activism. Mass deliberation describes the evolvement of new democratic institutions that aim to recruit citizens in direct participation. Two examples, the Citizens’ Council in Ostbelgien and the online ideation platform Better Reykjavík, are part of this new trend. Mass voting is another type of citizen participation that has received increasing popularity because of the Internet. Technological platforms make it easy to enable everyone to vote, such as the Five Star Movement does with its party members in Italy. Social media activism has also become increasingly important. This involves both informal political debate and political activism, which the social movement My Stealthy Freedom exemplifies. All these CI projects build on different conceptions of participatory diversity. In addition, transparent collective work is important in promoting intelligent engagement between large groups, both in scientific work like crowd peer review or political processes like the Icelandic constitutional experiment. However, there is a concern about the threats to democracy that dysfunctional engagement presents, such as fake news and echo chambers in social media.

Chapter 11 address how contributions are combined in different ways when designing CI. One approach utilizes many different perspectives in the same work, like in collective work on the same Wikipedia article. Multidisciplinary innovation teams also include a diversity of perspectives in creative problem solving. Second, contributions can be combined under the assumption that the golden middle way is the best solution. One example is the identification of a quantitative middle point, such as an average, that provides the most accurate solution if contributions are diverse. Another strategy is to find the middle way by developing a balanced representation of all sides, as in collective argument mapping. In addition, the middle way can identify commonalities, like the online environment vTaiwan that lets the crowd find consensual statements in political conflicts. A third approach scales up the number of contributions in the search for an unexpected solution. Many breakthrough ideas happen at the outskirts of a field. Online innovation contests aim to bring in creative outsiders or unknown others by inviting anyone to join. Furthermore, most of the contributions in CI projects build on a modularization strategy that splits a complex challenge into many smaller subtasks.

Chapter 12 describes different types of intelligent evaluation. At all group levels, most CI practices are reliant on some degree of explicit evaluation of the collective work. Digital technology also makes it possible to design metacommunicative feedback loops in most group work and organizational work. While some systems build on shared coordination, others let coordinators regulate the collective work. In the political system, intelligent evaluations are at the core of any well-functioning democratic system, from the nomothetai in ancient Athens to the Citizen Assembly in Ireland today. These new institutions strengthen citizen metadiscourses about important societal issues. A strong knowledge commons is also an important basic condition for this type of critical discourse. In general, digitized evaluations are becoming more common in society, exemplified by online reputation systems that rate a person’s trustworthiness, not only on business sites, but also in social media. However, there is increasing concern about the negative consequences of evaluating persons in the emerging reputation society.

Chapter 13 describes COVID-19 as a wicked problem and shows how different CI mechanisms have been used to cope with the pandemic. The first CI mechanism is the transparent flow of information during the pandemic. Knowledge is being shared at a rapid pace in the global online setting. Most of the big news sites provide citizens with updated statistics on the spread of the virus. Another example is the governmental “test and trace” strategy that aims to maximize information about the spread of the virus at all times. A second CI mechanism is citizen responsibility. Citizens in all countries have faced the challenge of complying with behavioral rules enforced by the government. Rules on social distancing and voluntary quarantines depend on citizen cooperation. Here, New Zealand stands out as one of the most successful countries. Third, collective learning at a system level has been important in dealing with the pandemic. One example is South Korea, which learned a lot from the Middle East Virus (MERS) in 2015 a couple of years before the COVID-19 outbreak. Their learning from the past failures in coping with that outbreak made them much better prepared than other countries.

Chapter 14 explores what motivates individuals to contribute to CI projects. If we think CI can benefit society, we need to understand what motivates individuals to engage in collective problem solving. However, both the complexity of the tasks and the required skill levels varies a lot. It ranges from innovation contests that often look for individuals with specific formal qualifications to citizen science project that require simple image detection skills. In a historical perspective, we have more spare time than ever before and many CI projects depend on this extra “time resource”, but the competition with social media and other entertainment services is fierce. The chapter examines a wide range of motivational factors, such as being immersed, being recognized, being part of a community, learning as motivation, economic motivation and making societal contributions. Statements from top solvers in online innovation contests comprise an important part of the content in this chapter.

Chapter 15 concludes by describing two radically different future visions of the intelligent society. On the one hand, instrumentarian intelligence assumes that algorithms tracking human behavior can predict human behavior more accurately than ever before. In Western countries, this intelligence manifests itself in a new surveillance capitalism, with companies like Google and Facebook constantly searching for behavioral surplus in both online and offline settings. In the political domain, instrumentarian intelligence seeks a reputation state built on a neobehavioristic governing model. The most prominent example is the nationwide social credit system in China that makes it possible to grade citizens on different behavioral indicators.

On the other hand, civic intelligence highlights a use of technology still controlled by the community and citizens, in contrast to the dehumanizing aspects of instrumentarian intelligence. While machine intelligence also craves for informational diversity in its hunt for behavioral surplus, civic intelligence seeks a broader diversity that includes not only information, but also multicultural, cognitive, biological and participatory diversity. The “fuel” of CI is people who are different from each other, with different interests and unique perspectives. Civic intelligence also builds on a strong knowledge commons and an open, shared collective memory. It does not hide information to produce the best predictions but promotes complete transparency and individual empowerment. In contrast to instrumentarian intelligence, CI still enables human-to-human intelligence, and not the algorithms, to be at the core of human collective problem solving.